III: Medium: Collaborative Research: Topological Data Analysis for Large Network Visualization

III：媒介：协作研究：大型网络可视化的拓扑数据分析

基本信息

批准号：
1513616
负责人：
Bei Phillips
金额：
$ 76.11万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513616&HistoricalAwards=false
关键词：
III Medium Collaborative Research Topological

项目摘要

This project leverages topological methods to develop a new class of data analysis and visualization techniques to understand the structure of networks. Networks are often used in modeling social, biological and technological systems, and capturing relationships among individuals, businesses, and genomic entities. Understanding such large, complex data sources is highly relevant and important in application areas including brain connectomics, epidemiology, law enforcement, public policy and marketing. The proposed research will be evaluated over multiple data sources, including but not limited to large social, communication and brain network datasets. Furthermore, the new approaches developed in this project will be integrated into growing data analysis curricula, shared through developing workshops, and used as topics to continue attracting underrepresented groups into STEM fields and computer science specifically. The scientific challenges this project addresses are two-fold: how to use topology to extract features from the data; and how to design effective visualizations to communicate these features to domain experts and decision makers. Topological techniques central to this project provide a strong theoretical basis for simplifying and summarizing complex data while still preserving critical underlying structures. They also provide a basis for task-oriented designs that allow us to control the volume of data to be displayed in visualizations, so users can develop faithful mental models of the data, facilitating information discovery. This project focuses on two research agendas. First, it proposes a rich body of topological summarization techniques to extract and preserve important topological features within large-scale graph-structured networks, and to obtain compact and hierarchical representations that are suitable for visual exploration. The feature extracting process captures complex interactions in the system, describes features at all scales, is robust with respect to noise, and has efficient computation. Second, this project proposes designing visualizations that encode the extracted topological structures explicitly, focusing on investigating techniques to fully exploit their properties in the visual metaphors to be developed. The project web site (http://www.sci.utah.edu/networktdav) provides additional information and will include access to developed tools and test data sets.

该项目利用拓扑方法开发一类新型数据分析和可视化技术来理解网络结构。网络通常用于对社会、生物和技术系统进行建模，并捕获个人、企业和基因组实体之间的关系。了解如此庞大、复杂的数据源对于脑连接组学、流行病学、执法、公共政策和营销等应用领域具有高度相关性和重要性。拟议的研究将在多个数据源上进行评估，包括但不限于大型社交、通信和大脑网络数据集。此外，该项目中开发的新方法将被纳入不断增长的数据分析课程中，通过举办研讨会进行共享，并用作继续吸引代表性不足的群体进入 STEM 领域和计算机科学的主题。该项目解决的科学挑战有两个：如何使用拓扑从数据中提取特征；以及如何设计有效的可视化来将这些功能传达给领域专家和决策者。该项目的核心拓扑技术为简化和总结复杂数据提供了强有力的理论基础，同时仍然保留了关键的基础结构。它们还为面向任务的设计提供了基础，使我们能够控制可视化中显示的数据量，以便用户可以开发忠实的数据心理模型，从而促进信息发现。该项目重点关注两个研究议程。首先，它提出了丰富的拓扑概括技术，以提取和保留大规模图结构网络中的重要拓扑特征，并获得适合视觉探索的紧凑和分层表示。特征提取过程捕获系统中复杂的交互，描述所有尺度的特征，对噪声具有鲁棒性，并且具有高效的计算。其次，该项目建议设计可视化，对提取的拓扑结构进行显式编码，重点研究技术以充分利用其在待开发的视觉隐喻中的特性。该项目网站 (http://www.sci.utah.edu/networktdav) 提供了更多信息，并将包括对已开发工具和测试数据集的访问。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Structural Average of Labeled Merge Trees for Uncertainty Visualization

用于不确定性可视化的标记合并树的结构平均值

DOI：
10.1109/tvcg.2019.2934242
发表时间：
2019-07-31
期刊：
IEEE Transactions on Visualization and Computer Graphics
影响因子：
5.2
作者：
Lin Yan;Yusu Wang;E. Munch;Ellen Gasparovic;Bei Wang
通讯作者：
Bei Wang

Moduli spaces of morse functions for persistence

持久性莫尔斯函数的模空间

DOI：
10.1007/s41468-020-00055-x
发表时间：
2020-06
期刊：
Journal of Applied and Computational Topology
影响因子：
0
作者：
Catanzaro, Michael J.;Curry, Justin M.;Fasy, Brittany Terese;Lazovskis, Jānis;Malen, Greg;Riess, Hans;Wang, Bei;Zabka, Matthew
通讯作者：
Zabka, Matthew

Sheaf-Theoretic Stratification Learning from Geometric and Topological Perspectives

从几何和拓扑角度进行层理论分层学习